Difference Between AI and ML
In this article, we will learn what Artificial Intelligence is, What Machine Learning is, their types, features, applications, and their differences.
Artificial Intelligence and Machine Learning are the two buzzwords of the 21st century. In the last decade, we have seen how technology is advancing exponentially. AI and ML are making the world more technology-driven, increasing productivity and efficiency. Both terms are interchangeable, but they are different.
AI is the broader concept of machines that enables computers to mimic human behavior. In contrast, ML is an application of AI based on the idea that we should be able to give machines access to data and let them learn for themselves. ML focuses on developing programs to teach themselves to grow and change when exposed to the data.
In simple terms, Machine Learning is the subset of Artificial Intelligence.
In this article, we will learn what artificial intelligence is, what machine learning is, and the types and features of AI and ML, and later, we will also explore how these two differ.
Table of Content
Difference Between AI and ML: AI vs ML
| Parameters | Artificial Intelligence | Machine Learning |
| Definition | AI is a broad field of computer science that aims to create systems capable of performing tasks that normally require human intelligence. | ML is a subset of AI that uses statistical techniques to enable machines to improve at tasks with experience. |
| Learning | AI can be programmed to do something intelligent. It doesn’t necessarily learn from its mistakes. | ML learns from its mistakes. As the algorithms receive more data, they improve their performance. |
| Data Usage | AI can or cannot use data as per the requirement. | ML always requires data to learn and improve. |
| Decision Making | AI works towards creating a system to mimic human behaviour and intelligence. | ML depends on AI. It creates algorithms to redefine the AI model. |
| Application | AI applications include Siri, Alexa, automated robots, etc. | ML applications include recommendation systems, spam filtering, fraud detection, etc. |
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What is Artificial Intelligence?
Artificial intelligence, or AI, is a broad field of computer science dedicated to creating systems that can perform tasks that normally require human intelligence. These tasks include learning from experience, understanding complex concepts, recognizing patterns, and making decisions. Artificial intelligence simulates human intelligence processes by combining learning, reasoning, problem-solving, perception and linguistic intelligence.
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Types of Artificial Intelligence
Different types of AI exist, but on a broader scale, it is classified into two categories based on capabilities and functionalities.
Based on Capabilities
- Weak AI (or Narrow AI): A type of AI designed to perform a specific or limited range of tasks. It is the most common and available AI. Some of the common examples are Play Chess, Translate Languages
- Example: Siri, Alex, Google Assistant, etc.
- General AI: A type of AI capable of performing any task that human can. It can learn and adapt to new situations based on reasoning and logic and make decisions as humans do.
- Example: ChatGPT, Google Bard
- Super AI: A type of AI that can surpass human intelligence. The super AI systems would be able to solve problems that are beyond human capabilities.
- Example: A hypothetical example could be a superintelligent doctor, that can diagnose diseases with near perfect accuracy by analyzing the patient symptoms, medical history, genetic data and relevant medical literature in seconds.
Based on Functionalities
- Reactive Machines: These machines can only react to their environment in the present moment. They have no memory or ability to learn.
- Limited Memory Machines: These machines remember past events and use that information to make decisions. They cannot understand the meaning of events and learn from them.
- Theory of Mind Machines: These machines can understand the thoughts and intentions of others. This allows them to collaborate with others and plan for the future.
- Self-Aware Machines: These machines completely understand themselves and their place in the world. They can make decisions based on their goals and values.
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Features of Artificial Intelligence
- Problem-Solving: AI can be programmed to solve complex problems and make decisions based on rules or algorithms.
- Learning: AI systems can learn and adapt to new inputs, improving performance.
- Perception: AI can interpret the environment using computer vision and speech recognition technologies.
- Manipulation and Navigation: Some AI systems, such as robots, can interact with the physical world.
What is Machine Learning?
Machine Learning is a branch of Artificial Intelligence that works with two major components – algorithms and relevant data. These machine learning algorithms’ learning process is based on your data. The more high-quality data you give your algorithm or machine learning model, the more accurate its forecast/prediction.
In other words, the accuracy of your machine learning model improves with better quality and quantity of relevant data.
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Types of Machine Learning
- Supervised Learning: Supervised Learning is when the model learns under supervision or guidance. Here supervision means a target value or label. In other words, the models or the algorithm is trained on labelled data sets. It is majorly used for prediction (classification/ regression) when the class number is known and fixed.
- Unsupervised Learning: Unsupervised learning is based on finding meaningful patterns and groups in unlabeled data based on features and purposes. In other words, unlabeled data points are used as references to find meaningful structures and patterns in the observations.
- Reinforcement Learning: Reinforcement learning is about taking suitable action to maximize reward in a particular situation. Simply put, there’s an agent and a reward with many hurdles in between.
Features of Machine Learning
- Pattern Recognition: ML algorithms can automatically recognize and analyze patterns in large data sets.
- Anomaly detection: ML can detect unusual patterns or deviations from the norm.
- Prediction: ML can make predictions about future outcomes based on historical data.
- Automation: ML algorithms can learn and make decisions without human intervention.
Key Differences and Similarities Between Artificial Intelligence and Machine Learning
- AI allows a machine to simulate human intelligence to solve problems. In contrast, ML allows a machine to learn autonomously from past data.
- AI aims to develop a system that can perform complex tasks, whereas ML aims to build a machine that can learn from past data and increase accuracy.
- ML uses self-learning algorithms to produce predictive models, whereas AI uses technology in the system to mimic human decisions.
- ML uses only structured and semi-structured data, whereas AI uses structured, semi-structured, and unstructured data.
Conclusion
In this article, we have discussed what Artificial Intelligence is, What Machine Learning is, their types, features, applications, and their differences.
Hope you will like the article.
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FAQs
What is Artificial Intelligence?
Artificial intelligence, or AI, is a broad field of computer science dedicated to creating systems that can perform tasks that normally require human intelligence. These tasks include learning from experience, understanding complex concepts, recognizing patterns, and making decisions. Artificial intelligence simulates human intelligence processes by combining learning, reasoning, problem-solving, perception and linguistic intelligence.
What is Machine Learning?
Machine Learning is a branch of Artificial Intelligence that works with two major components algorithms and relevant data. This machine learning algorithms' learning process is based on your data. The more high-quality data you give your algorithm or machine learning model, the more accurate its forecast/prediction. In other words, the accuracy of your machine learning model improves with better quality and quantity of relevant data.
How do AI and ML differ in terms of their definition and scope?
AI is a broad field of computer science that aims to create systems capable of performing tasks that normally require human intelligence. ML is a subset of AI that uses statistical techniques to enable machines to improve at tasks with experience.
Are Machine Learning and Artificial Intelligence the same thing?
No, both AI and ML are not same. AI is the broader concept of machines that enables computers to mimic human behavior. In contrast, ML is an application of AI based on the idea that we should be able to give machines access to data and let them learn for themselves. ML focuses on developing programs to teach themselves to grow and change when exposed to the data.
How does Machine Learning fit within Artificial Intelligence?
Machine Learning is a core part of AI. It provides the fundamental building blocks - learning, adaptation, and evolution - that enable AI systems to learn from data and improve their performance over time without being explicitly programmed to do so.
Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio
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